Multivariate autoregressive modeling and granger causality analysis of multiple spike trains

Authors:
Michael Krumin;Shy Shoham
Affiliations:
Faculty of Biomedical Engineering, Technion--Israel Institute of Technology, Haifa, Israel;Faculty of Biomedical Engineering, Technion--Israel Institute of Technology, Haifa, Israel
Venue:
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Year:
2010

Citing 13
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Abstract

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting "hidden" Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.